129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from unittest.mock import Mock
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import torch
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from pytorch_lightning import Trainer
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from tests.helpers import BoringModel
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class HPCHookdedModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.hpc_save_called = 0
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self.hpc_load_called = 0
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def on_hpc_save(self, checkpoint):
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assert "state_dict" in checkpoint
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self.hpc_save_called += 1
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def on_hpc_load(self, checkpoint):
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assert "state_dict" in checkpoint
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self.hpc_load_called += 1
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def test_hpc_hook_calls(tmpdir):
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model = HPCHookdedModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
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trainer.fit(model)
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connector = trainer.checkpoint_connector
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connector.hpc_save(tmpdir, logger=Mock())
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assert model.hpc_save_called == 1
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assert model.hpc_load_called == 0
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# new training run, restore from hpc checkpoint file automatically
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assert set(os.listdir(tmpdir)) == {"hpc_ckpt_1.ckpt"}
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
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trainer.fit(model)
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assert model.hpc_save_called == 1
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assert model.hpc_load_called == 1
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def test_preloaded_checkpoint_lifecycle(tmpdir):
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"""Tests that the preloaded checkpoint contents gets cleared from memory when it is not required anymore."""
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1)
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trainer.fit(model)
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connector = trainer.checkpoint_connector
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assert not trainer.resume_from_checkpoint
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assert not connector.resume_checkpoint_path
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assert not connector._loaded_checkpoint
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connector.resume_start()
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assert not connector.resume_checkpoint_path
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assert not connector._loaded_checkpoint
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connector.resume_end()
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assert not connector.resume_checkpoint_path
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assert not connector._loaded_checkpoint
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ckpt_path = trainer.checkpoint_callback.best_model_path
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, resume_from_checkpoint=ckpt_path)
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connector = trainer.checkpoint_connector
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connector.resume_start()
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assert connector.resume_checkpoint_path == ckpt_path
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assert connector._loaded_checkpoint
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assert isinstance(connector._loaded_checkpoint, dict)
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connector.resume_end()
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assert not connector.resume_checkpoint_path
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assert not connector._loaded_checkpoint
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def test_hpc_restore_attempt(tmpdir):
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"""Test that restore() attempts to restore the hpc_ckpt with highest priority."""
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, checkpoint_callback=False, logger=False)
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trainer.fit(model)
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hpc_ckpt_path = tmpdir / "hpc_ckpt_3.ckpt"
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trainer.save_checkpoint(hpc_ckpt_path)
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assert os.listdir(tmpdir) == ["hpc_ckpt_3.ckpt"]
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# set weights to zero
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for param in model.parameters():
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torch.nn.init.constant_(param, 0)
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# case 1: restore hpc first, no explicit resume path provided
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trainer = Trainer(default_root_dir=tmpdir, max_steps=2, checkpoint_callback=False, logger=False)
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trainer.fit(model)
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for param in model.parameters():
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assert param.abs().sum() > 0
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torch.nn.init.constant_(param, 0)
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# case 2: explicit resume path provided, restore hpc anyway
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trainer = Trainer(default_root_dir=tmpdir, max_steps=3, resume_from_checkpoint="not existing")
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trainer.fit(model)
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for param in model.parameters():
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assert param.abs().sum() > 0
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def test_hpc_max_ckpt_version(tmpdir):
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"""Test that the CheckpointConnector is able to find the hpc checkpoint file with the highest version."""
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1)
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trainer.fit(model)
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trainer.save_checkpoint(tmpdir / "hpc_ckpt.ckpt")
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trainer.save_checkpoint(tmpdir / "hpc_ckpt_0.ckpt")
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trainer.save_checkpoint(tmpdir / "hpc_ckpt_3.ckpt")
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trainer.save_checkpoint(tmpdir / "hpc_ckpt_33.ckpt")
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assert trainer.checkpoint_connector.hpc_resume_path == str(tmpdir / "hpc_ckpt_33.ckpt")
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assert trainer.checkpoint_connector.max_ckpt_version_in_folder(tmpdir) == 33
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assert trainer.checkpoint_connector.max_ckpt_version_in_folder(tmpdir / "not" / "existing") is None
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